Many clustering algorithms are prone to producing trivial solutions and empty clusters [1]}, [2]}. Distribution regularizers can significantly improve clustering performance [1]}, [4]} in these situations.
Dizaji et al. used a regularization term to penalize non-uniform cluster assignments. In our case, the overall distribution of the data is unknown and we can not assume that the distribution should be uniform. Instead, we apply a regularizer on each view to avoid empty clusters and maintain freedom for the overall distribution. We define a target label distribution by averaging the soft frequencies for every view.
\(m_j = q(y = j) &= \frac{1}{N}\sum ^N_i r_{ij} \\n_j = r(y = j) &= \frac{1}{N}\sum ^N_i q_{ij}\) 
